Genetic correlations and causal relationships between cardio-metabolic traits and sepsis

Cardio-metabolic traits have been reported to be associated with the development of sepsis. It is, however, unclear whether these co-morbidities reflect causal associations, shared genetic heritability, or are confounded by environmental factors. We performed three analyses to explore the relationships between cardio-metabolic traits and sepsis. Mendelian randomization (MR) study to evaluate the causal effects of multiple cardio-metabolic traits on sepsis. Global genetic correlation analysis to explore the correlations between cardio-metabolic traits and sepsis. Local genetic correlation (GC) analysis to explore shared genetic heritability between cardio-metabolic traits and sepsis. Some loci were further examined for related genes responsible for the causal relationships. Genetic associations were obtained from the UK Biobank data or published large-scale genome-wide association studies with sample sizes between 200,000 to 750,000. In MR, we found causality between BMI and sepsis (OR: 1.53 [1.4–1.67]; p < 0.001). Body mass index (BMI), which is confirmed by sensitivity analyses and multivariable MR adjusting for confounding factors. Global GC analysis showed a significant correlation between BMI and sepsis (rg = 0.55, p < 0.001). More cardio-metabolic traits were identified to be correlated to the sepsis onset such as CRP (rg = 0.37, p = 0.035), type 2 diabetes (rg = 0.33, p < 0.001), HDL (rg = − 0.41, p < 0.001), and coronary artery disease (rg = 0.43, p < 0.001). Local GC revealed some shared genetic loci responsible for the causality. The top locus 1126 was located at chromosome 7 and comprised genes HIBADH, JAZF1, and CREB5. The present study provides evidence for an independent causal effect of BMI on sepsis. Further detailed analysis of the shared genetic heritability between cardio-metabolic traits and sepsis provides the opportunity to improve the preventive strategies for sepsis.

the instrumental variables.To do so, we selected GWAS significant SNPs with p < 5 × 10 −8 and then performed LD clumping with LD r 2 < 0.001 within a 10,000 kb window.The secondary clumping threshold was p = 5 × 10 −8 .The extracted SNPs were then queried against the requested outcome of sepsis/sepsis (under 75).If a particular SNP is not present in the outcome dataset then it is possible to use SNPs that are LD 'proxies' instead.The proxies (LD tags) with minimum LD r2 value of 0.8 were looked for, and the tag alleles were aligned to target alleles.The effect of an SNP on an outcome and exposure were then harmonized to be relative to the same allele.The heterogeneity statistics were reported to assess the robustness of the causal relationships.The result from each SNP was considered an independent RCT, and the results from all SNPs were pooled with a meta-analytic approach to obtain an overall causal estimate 17,18 .The effect size for each meta-analysis is reported in the main results as the effect of a one-standard deviation (1-SD) change in continuous traits (log transformation was applied if necessary).To examine whether the effect of BMI was independently associated with sepsis, we performed multivariable MR analysis.For each exposure, the instruments are selected then all exposures for those SNPs are regressed against the outcome together, weighting for the inverse variance of the outcome.
Pleiotropy is the phenomenon of a single genetic variant influencing multiple traits, which can lead to a false positive conclusion, we used multiple MR methods for the causal effect estimations, such as MR-Egger, weighted median, inverse variance weighted, simple mode, and weighted mode.We evaluated the directional pleiotropy based on the intercept obtained from the MR-Egger analysis 19 .We also performed a leave-one-out analysis in which we sequentially omitted one SNP at a time, to evaluate whether the MR estimate was driven or biased by a single SNP.The TwoSampleMR (v0.5.6) package was employed for this analysis.We follow the reporting guideline Strengthening the reporting of observational studies in epidemiology using the Mendelian randomization (STROBE-MR) 20 .

Global genetic correlation analysis
The above-mentioned Mendelian randomization uses significantly associated SNPs as instrumental variables to quantify causal relationships between the exposure and outcome.This is effective for traits where many significant associations account for a substantial fraction of heritability.However, heritability is distributed over thousands of variants with small effects for many complex traits, thus genetic correlation was performed by using genomewide data rather than data for only significantly associated variants to obtain more accurate results.Global genetic correlation (r g ) analysis was performed using the cross-trait LD Score regression 10 .The method relies on the fact that the GWAS effect size estimate for a given SNP incorporates the effects of all SNPs in linkage disequilibrium (LD) with that SNP.For a polygenic trait, SNPs with high LD will have higher χ 2 statistics on average than SNPs with low LD.A similar relationship holds if we replace the χ 2 statistics for a single study with the product of the z scores from two studies of traits with non-zero genetic correlation.The python package LDSC (LD Score; v1.0.1) was employed for the analysis.

Local genetic correlation analysis
A global r g represents an average of the shared association across the genome, local r g s in opposing directions could result in a nonsignificant global r g , and local r g s in the absence of any global relation may be undetected.Thus, we performed local genetic correlation analysis by using the LAVA (Local Analysis of [co]Variant Association) 21 .Sample overlap was estimated using the intercepts from bivariate LDSC.The European panel of phase 3 of 1000 Genomes (MAF > 0.5%) was employed as an LD reference 22 .The genomic loci were created by partitioning the genome into blocks of approximately equal size (~ 1 Mb) while minimizing the LD between them.For each phenotype pair (traits versus sepsis), the loci were first filtered by the univariate test so that both phenotypes exhibited univariate signal at Holm-corrected P < 0.05.Multivariate genetic association analysis can be performed via either partial correlation or multiple regression.The analysis was performed by the R package LAVA (v0.1.0) 21.

Ethics approval and consent to participate
The study was conducted by secondary analysis of data from other studies, and informed consent was obtained from participants or their family members in the original studies.

The causal association between cardio-metabolic traits and sepsis
Genetically predicted larger BMI (each 1 SD increase) was associated with a significantly higher risk of sepsis (OR: 1.53 [1.4-1.67];p < 0.001 by IVW method).As expected, the associations were consistent in sensitivity analyses using the MR-Egger method (OR: 1.49 [1.18-1.88];p < 0.001) and the weighted median method (OR: 1.5 [1.29-1.74];p < 0.001, Fig. 1).But the latter two methods provided less precise estimates than that with the conventional IVW method.In a leave-one-out sensitivity analysis, we found that no single SNP was strongly driving the overall effect of BMI on sepsis (Fig. 2A,C).The MR regression slopes are illustrated in Fig. 2B.There was no evidence for the presence of directional pleiotropy in the MR-Egger regression analysis, the P-values for the intercepts were large and the estimates adjusted for pleiotropy suggested null effects (Egger Intercept = 0.00047, p = 0.81; SDC Table S1).These results were in line with the hypothesis that genetic pleiotropy was not driving the result.No significant heterogeneity was identified for the causal effect of BMI on sepsis (Q = 511 for MR-Egger; p = 0.123; Q = 511 for IVW method, p = 0.129, SDC Table S2).
To examine whether the effect of BMI was independently associated with sepsis, we performed multivariable MR analysis.The results showed that BMI was independently associated with sepsis risk (adjusted OR: 1.29; 95% CI: 1.09-1.52),while other cardio-metabolic traits were no longer associated with the sepsis risk (Fig. 3).Similar results were reproduced by restricting to sepsis under 75 years old (adjusted OR: 1.21; 95% CI: 1.04-1.41),although the magnitude was lower.This result indicated that the causal effects of type 2 diabetes, LDL, and HDL could be explained by BMI.

Global genetic correlation analysis
Since sepsis is a complex trait and its development is driven by thousands of genetic variants, with small effects from each of these variants.Thus, the genetic correlation was performed by using genome-wide data rather than data for only significantly associated variants to obtain more accurate results (SDC Table S4).As compared with the MR analysis, more cardio-metabolic traits were identified to be correlated to the sepsis onset such as CRP (r g = 0.37, p = 0.035), type 2 diabetes (r g = 0.33, p < 0.001), HDL (r g = − 0.41, p < 0.001), coronary artery disease (r g = 0.43, p < 0.001), and BMI (r g = 0.55, p < 0.001).The results were consistent in sepsis under 75 (Fig. 4A).There were other cross-trait correlation pairs such as type 2 diabetes and HDL cholesterol, CRP and BMI (Fig. 4B).

Local genetic correlation analysis
We applied LAVA to sepsis outcome and cardio-metabolic traits (Table 1), testing the pairwise local r g s within 2495 genomic loci (genome-wide).The genomic loci were created by partitioning the genome into blocks of approximately equal size (~ 1 Mb) while minimizing the LD between them, and the genomic coordinates are in reference to the human genome build 37. Sample overlap was estimated using the intercepts from bivariate LDSC obtained in the above section.With a Holm-corrected p < 0.05, we detected 572 significant bivariate local r g s across 318 loci, of which 140 loci were associated with more than one phenotype pair.Figure 5A shows the correlation between cardio-metabolic traits and sepsis outcome.The correlation strength as measured by the number of significant local r g s was consistent for sepsis and sepsis under 75.BMI showed the largest number of significant r g s, followed by HDL, CRP, and CAD.For most significant correlations, 95% confidence intervals (CIs) for the explained variance included 1, consistent with the scenario that the local genetic signal of those phenotypes is completely shared (Fig. 5B).
We further displayed three top loci that had the largest number of significant correlations to examine possible genes driving these traits (Fig. 6A-C).The locus 1126 had the greatest number of significant r g s, which showed positive r g s for BMI and CAD, and negative r g s for HDL and eosinophil cell count (Fig. 6B).The locus complex traits such as sepsis, there can be thousands of SNPs with small effects responsible for the heritability, thus global GC can help to address this issue.Cardio-metabolic traits have been explored in other epidemiological studies for their associations with the risk of sepsis development and/or sepsis severity.For example, in a large multi-center cohort study, lower BMI (< 20 kg/m 2 ) was associated with reduced mortality in patients with bloodstream infection 25 .A compelling body of evidence from MR studies has significantly contributed to our understanding of the relationship between obesity and sepsis 26,27 .The pathogenetic pathways connecting BMI or obesity to sepsis risk are multifaceted.Chronic low-grade inflammation, altered immune responses, and metabolic dysregulation have emerged as key contributors [28][29][30] .Studies have elucidated the impact of adipose tissue-derived inflammatory mediators on immune function, potentially predisposing obese individuals to an exaggerated inflammatory response during infections 31,32 .However, studies conducted in critical care settings showed that greater BMI was associated with improved survival, which is known as the obesity paradox in the intensive care unit (ICU) [33][34][35] .Probably, the pathophysiology of critical illness is different from those in the non-critical care setting.Critically ill patients are more likely to benefit from a greater BMI and long-term exposure to low-grade metabolic inflammation.Possible pathological mechanisms underlying the obesity paradox included higher energy reserves, inflammatory preconditioning, anti-inflammatory immune profile, and endotoxin neutralization 36 .Furthermore, our study focused on the sepsis predisposition rather than the mortality risk after the development of sepsis.It should be emphasized that susceptibility to sepsis is not equivalent to sepsis severity.Epidemiological studies for sepsis predisposition are usually performed in the patient population who are not critically ill, and long-term exposure to metabolic inflammation increases the risk of sepsis 37,38 .
Although the MR technique employed genetic variants as the IV, which is less likely to be affected by environmental confounding factors.Violations to other IV criteria are still great threats to causal inference, such as the pleiotropic effects of genetic variants.To account for this bias, we first employed Egger's method, which failed to identify statistically significant pleiotropic effects.The results were robust in sensitivity analysis restricting to sepsis under 75.Then, we performed multivariable MR analysis using covariates known to be associated with sepsis such as CRP, type 2 diabetes, and neutrophil counts.After covariate adjustment, BMI remains to be independently associated with sepsis.furthermore, we also performed a leave-one-off analysis to test whether there are SNPs that significantly drive the results.The results revealed that there was no single SNP strongly driving the overall effect of BMI on sepsis.
Although MR analysis consistently showed causal effects of BMI on sepsis predisposition, it was not able to reveal underlying mechanisms responsible for the association.Local GC analysis may help to reveal some www.nature.com/scientificreports/potential pathways mediating the linkage.By examining genes residing within the top loci, we identified some potential pathways related to inflammatory responses.For example, in the top locus 1126, we found several genes that are playing key roles in inflammatory responses including HIBADH, JAZF1, and CREB5.JAZF1 encodes a nuclear protein with three C2H2-type zinc fingers and functions as a transcriptional repressor.Genetic variations in this gene are correlated with decreased body mass index (BMI) and waist circumference 39,40 .Further experimental studies confirmed its important role in adipocyte differentiation, obesity, insulin resistance, and inflammation 41,42 .
In conclusion, our MR study establishes the causal effects of increased BMI on sepsis development.While more work is needed to understand the pathophysiology explaining these associations, an underlying derangement in inflammation should be suspected.

30 Method: Inverse variance weightedFigure 1 .Figure 2 .
Figure1.Forest plots showing the causal effects of cardio-metabolic traits on the risk of sepsis.inverse variance weights estimates were performed.Sensitivity analysis was performed by restricting to sepsis under 75 years old.

Figure 3 .
Figure 3. Multivariable MR analysis to adjust for possible confounding factors.The error bar indicates a 95% confidence interval.

o l e s t e r o l t r i g l y c e r i d e s b a s o p h i l c e l l c o u n t w h i t e b l o o d c e l l c o u n t m o n o c y t e c e l l c o u n t l y m p h o c y t e c e l l c o u n t e o s i n o p h i l c e l l c o u n t n e u t r o p h i l c e l l c o u nFigure 4 .Figure 5 .
Figure 4. Global genetic correlations across sepsis and cardio-metabolic traits.(A) genetic correlation for top pairs of cardio-metabolic traits and sepsis; (B) Heatmap plot showing the genetic correlation across each pair of traits.

Figure 6 .
Figure 6.Sample loci with the top number of significant traits.(A) The top 3 loci with the largest number of significant traits; genetic correlation network between traits for locus 1126 (B) and 2036 (C).The red color indicates a negative correlation, and the blue color indicates a positive correlation.The number on the line indicates the genetic correlation (r g ).Each green node represents a trait.

Table 1 .
Data used for the Mendelian randomization analysis.For categorical outcome data participant numbers were split into cases and controls.